




'''simple docstring'''



import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowerCamelCase_									=		logging.get_logger(__name__)

lowerCamelCase_									=		{
    """ut/deta""": """https://huggingface.co/ut/deta/resolve/main/config.json""",
}







class 						lowercase_       (					UpperCAmelCase__  ):


   """simple docstring"""

   lowerCamelCase_							    =		'''deta'''
   lowerCamelCase_							    =		{
       '''hidden_size''': '''d_model''',
       '''num_attention_heads''': '''encoder_attention_heads''',
   }



   def __init__(       self   :      str    , __lowerCamelCase   :      Tuple=None    , __lowerCamelCase   :      Any=9_0_0    , __lowerCamelCase   :      Optional[Any]=2_0_4_8    , __lowerCamelCase   :      Any=6    , __lowerCamelCase   :      int=2_0_4_8    , __lowerCamelCase   :      Optional[int]=8    , __lowerCamelCase   :      Any=6    , __lowerCamelCase   :      Tuple=1_0_2_4    , __lowerCamelCase   :      Optional[Any]=8    , __lowerCamelCase   :      List[str]=0.0    , __lowerCamelCase   :      Dict=True    , __lowerCamelCase   :      List[Any]="relu"    , __lowerCamelCase   :      Union[str, Any]=2_5_6    , __lowerCamelCase   :      Optional[Any]=0.1    , __lowerCamelCase   :      int=0.0    , __lowerCamelCase   :      Union[str, Any]=0.0    , __lowerCamelCase   :      Union[str, Any]=0.0_2    , __lowerCamelCase   :      List[Any]=1.0    , __lowerCamelCase   :      int=True    , __lowerCamelCase   :      List[str]=False    , __lowerCamelCase   :      Tuple="sine"    , __lowerCamelCase   :      str=5    , __lowerCamelCase   :      Dict=4    , __lowerCamelCase   :      int=4    , __lowerCamelCase   :      Tuple=True    , __lowerCamelCase   :      Union[str, Any]=3_0_0    , __lowerCamelCase   :      str=True    , __lowerCamelCase   :      Optional[int]=True    , __lowerCamelCase   :      Optional[Any]=1    , __lowerCamelCase   :      Dict=5    , __lowerCamelCase   :      Dict=2    , __lowerCamelCase   :      Any=1    , __lowerCamelCase   :      Union[str, Any]=1    , __lowerCamelCase   :      Dict=5    , __lowerCamelCase   :      Union[str, Any]=2    , __lowerCamelCase   :      Tuple=0.1    , __lowerCamelCase   :      Tuple=0.2_5    , **__lowerCamelCase   :      str    , ):

      """simple docstring"""
      if backbone_config is None:
         logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."       )
         _SCREAMING_SNAKE_CASE			= CONFIG_MAPPING["resnet"](out_features=["stage2", "stage3", "stage4"]       )
      else:
         if isinstance(_a    , _a       ):
            _SCREAMING_SNAKE_CASE			= backbone_config.pop("model_type"       )
            _SCREAMING_SNAKE_CASE			= CONFIG_MAPPING[backbone_model_type]
            _SCREAMING_SNAKE_CASE			= config_class.from_dict(_a       )

      _SCREAMING_SNAKE_CASE			= backbone_config
      _SCREAMING_SNAKE_CASE			= num_queries
      _SCREAMING_SNAKE_CASE			= max_position_embeddings
      _SCREAMING_SNAKE_CASE			= d_model
      _SCREAMING_SNAKE_CASE			= encoder_ffn_dim
      _SCREAMING_SNAKE_CASE			= encoder_layers
      _SCREAMING_SNAKE_CASE			= encoder_attention_heads
      _SCREAMING_SNAKE_CASE			= decoder_ffn_dim
      _SCREAMING_SNAKE_CASE			= decoder_layers
      _SCREAMING_SNAKE_CASE			= decoder_attention_heads
      _SCREAMING_SNAKE_CASE			= dropout
      _SCREAMING_SNAKE_CASE			= attention_dropout
      _SCREAMING_SNAKE_CASE			= activation_dropout
      _SCREAMING_SNAKE_CASE			= activation_function
      _SCREAMING_SNAKE_CASE			= init_std
      _SCREAMING_SNAKE_CASE			= init_xavier_std
      _SCREAMING_SNAKE_CASE			= encoder_layerdrop
      _SCREAMING_SNAKE_CASE			= auxiliary_loss
      _SCREAMING_SNAKE_CASE			= position_embedding_type
      # deformable attributes
      _SCREAMING_SNAKE_CASE			= num_feature_levels
      _SCREAMING_SNAKE_CASE			= encoder_n_points
      _SCREAMING_SNAKE_CASE			= decoder_n_points
      _SCREAMING_SNAKE_CASE			= two_stage
      _SCREAMING_SNAKE_CASE			= two_stage_num_proposals
      _SCREAMING_SNAKE_CASE			= with_box_refine
      _SCREAMING_SNAKE_CASE			= assign_first_stage
      if two_stage is True and with_box_refine is False:
         raise ValueError("If two_stage is True, with_box_refine must be True."       )
      # Hungarian matcher
      _SCREAMING_SNAKE_CASE			= class_cost
      _SCREAMING_SNAKE_CASE			= bbox_cost
      _SCREAMING_SNAKE_CASE			= giou_cost
      # Loss coefficients
      _SCREAMING_SNAKE_CASE			= mask_loss_coefficient
      _SCREAMING_SNAKE_CASE			= dice_loss_coefficient
      _SCREAMING_SNAKE_CASE			= bbox_loss_coefficient
      _SCREAMING_SNAKE_CASE			= giou_loss_coefficient
      _SCREAMING_SNAKE_CASE			= eos_coefficient
      _SCREAMING_SNAKE_CASE			= focal_alpha
      super().__init__(is_encoder_decoder=_a    , **_a       )



   @property
   def 				lowerCAmelCase_     (       self   :      int       ):

      """simple docstring"""
      return self.encoder_attention_heads



   @property
   def 				lowerCAmelCase_     (       self   :      Union[str, Any]       ):

      """simple docstring"""
      return self.d_model



   def 				lowerCAmelCase_     (       self   :      Tuple       ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE			= copy.deepcopy(self.__dict__       )
      _SCREAMING_SNAKE_CASE			= self.backbone_config.to_dict()
      _SCREAMING_SNAKE_CASE			= self.__class__.model_type
      return output







"""simple docstring"""




def        a__					(							snake_case__ = 50_00_00_00							)		->   int:
			lowerCamelCase      =   set()
			lowerCamelCase      =   int((limit - 24) ** (1 / 2)							)

			lowerCamelCase      =   set(range(3			,					prime_square_limit + 1			,					2							)							)
			primes.add(2							)
			for p in range(3			,					prime_square_limit + 1			,					2							):
						if p not in primes:
									continue
						primes.difference_update(set(range(p * p			,					prime_square_limit + 1			,					snake_case__							)							)							)

			for primea in primes:
						lowerCamelCase      =   primea * primea
						for primea in primes:
									lowerCamelCase      =   primea * primea * primea
									if square + cube >= limit - 16:
												break
									for primea in primes:
												lowerCamelCase      =   primea * primea * primea * primea
												lowerCamelCase      =   square + cube + tetr
												if total >= limit:
															break
												ret.add(snake_case__							)

			return len(snake_case__							)


if __name__ == "__main__":
					print(F"""{solution() = }""")



